Four-Dimensional Computed Tomography Ventilation Image-Guided Lung Functional Avoidance Radiation Therapy: A Single-Arm Prospective Pilot Clinical Trial.

PURPOSE: The primary objective of this prospective pilot trial was to assess the safety and feasibility of lung functional avoidance radiation therapy (RT) with 4-dimensional (4D) computed tomography (CT) ventilation imaging. METHODS AND MATERIALS: Patients with primary lung cancer or metastatic disease to the lungs to receive conventionally fractionated RT (CFRT) or stereotactic body RT (SBRT) were eligible. Standard-of-care 4D-CT scans were used to generate ventilation images through image processing/analysis. Each patient required a standard intensity modulated RT plan and ventilation image guided functional avoidance plan. The primary endpoint was the safety of functional avoidance RT, defined as the rate of grade ≥3 adverse events (AEs) that occurred ≤12 months after treatment. Protocol treatment was considered safe if the rates of grade ≥3 pneumonitis and esophagitis were <13% and <21%, respectively for CFRT, and if the rate of any grade ≥3 AEs was <28% for SBRT. Feasibility of functional avoidance RT was assessed by comparison of dose metrics between the 2 plans using the Wilcoxon signed-rank test. RESULTS: Between May 2015 and November 2019, 34 patients with non-small cell lung cancer were enrolled, and 33 patients were evaluable (n = 24 for CFRT; n = 9 for SBRT). Median follow-up was 14.7 months. For CFRT, the rates of grade ≥3 pneumonitis and esophagitis were 4.2% (95% confidence interval, 0.1%-21.1%) and 12.5% (2.7%-32.4%). For SBRT, no patients developed grade ≥3 AEs. Compared with the standard plans, the functional avoidance plans significantly (P < .01) reduced the lung dose-function metrics without compromising target coverage or adherence to standard organs at risk constraints. CONCLUSIONS: This study, representing one of the first prospective investigations on lung functional avoidance RT, demonstrated that the 4D-CT ventilation image guided functional avoidance RT that significantly reduced dose to ventilated lung regions could be safely administered, adding to the growing body of evidence for its clinical utility.

Quantitative assessment of ventilation-perfusion relationships with gallium-68 positron emission tomography/computed tomography imaging in lung cancer patients.

Pulmonary functional imaging has demonstrated potential to improve thoracic radiotherapy. The purpose of this study was twofold: 1) to quantify ventilation/perfusion relationships in lung cancer patients using a new functional imaging approach, gallium-68 (68Ga)-positron emission tomography/computed tomography (PET/CT); and 2) to compare ventilation/perfusion matching with diffusing capacity of the lung for carbon monoxide (DLCO). Voxel-wise correlations between ventilation and perfusion varied widely among 19 patients (range: 0.26-0.88). 68Ga-PET/CT-measured percent gas exchanging lung volume was moderately correlated with DLCO (≤0.59). Our findings suggested that 68Ga-PET/CT ventilation/perfusion imaging provided complementary information and a reasonable surrogate for gas exchange in lung cancer patients.

Prognostic Value of Computed Tomography and/or 18F-Fluorodeoxyglucose Positron Emission Tomography Radiomics Features in Locally Advanced Non-small Cell Lung Cancer.

INTRODUCTION: We investigated whether adding computed tomography (CT) and/or 18F-fluorodeoxyglucose (18F-FDG) PET radiomics features to conventional prognostic factors (CPFs) improves prognostic value in locally advanced non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: We retrospectively identified 39 cases with stage III NSCLC who received chemoradiotherapy and underwent planning CT and staging 18F-FDG PET scans. Seven CPFs were recorded. Feature selection was performed on 48 CT and 49 PET extracted radiomics features. A penalized multivariate Cox proportional hazards model was used to generate models for overall survival based on CPFs alone, CPFs with CT features, CPFs with PET features, and CPFs with CT and PET features. Linear predictors generated and categorized into 2 risk groups for which Kaplan-Meier survival curves were calculated. A log-rank test was performed to quantify the discrimination between the groups and calculated the Harrell's C-index to quantify the discriminatory power. A likelihood ratio test was performed to determine whether adding CT and/or PET features to CPFs improved model performance. RESULTS: All 4 models significantly discriminated between the 2 risk groups. The discriminatory power was significantly increased when CPFs were combined with PET features (C-index 0.82; likelihood ratio test P < .01) or with both CT and PET features (0.83; P < .01) compared with CPFs alone (0.68). There was no significant improvement when CPFs were combined with CT features (0.68). CONCLUSION: Adding PET radiomics features to CPFs yielded a significant improvement in the prognostic value in locally advanced NSCLC; adding CT features did not.

Characterization and clinical validation of patient-specific three-dimensional printed tissue-equivalent bolus for radiotherapy of head and neck malignancies involving skin.

PURPOSE: Megavoltage radiotherapy to irregular superficial targets is challenging due to the skin sparing effect. We developed a three-dimensional bolus (3DB) program to assess the clinical impact on dosimetric and patient outcomes. MATERIALS AND METHODS: Planar commercial bolus (PCB) and 3DB density, clarity, and net bolus effect were rigorously evaluated prior to clinical implementation. After IRB approval, patients with cutaneous or locally advanced malignancies deemed to require bolus for radiotherapy treatment were treated with custom 3DB. RESULTS: The mean density of 3DB and PCB was of 1.07 g/cm 3 and 1.12 g/cm3, respectively. 3DB optic clarity was superior versus PCB at any material thickness. Phantom measurements of superficial dose with 3DB and PCB showed excellent bolus effect for both materials. 3DB reduced air gaps compared with PCB - particularly in irregular areas such as the ear, nose, and orbit. A dosimetric comparison of 3DB and PCB plans showed equivalent superficial homogeneity for 3DB and PCB (3DB median HI 1.249, range 1.111-1.300 and PCB median HI 1.165, range 1.094-1.279), but better conformity with 3DB (3DB median CI 0.993, range 0.962-0.993) versus PCB (PCB median CI 0.977, range 0.601-0.991). Patient dose measurements using 3DB confirm the delivered superficial dose was within 1% of the intended prescription (95% CI 97-102%; P = 0.11). CONCLUSIONS: 3DB improves radiotherapy plan conformity, reduces air gap volume in irregular superficial areas which could affect superficial dose delivery, and provides excellent dose coverage to irregular superficial targets.

Variations Between Dose-Ventilation and Dose-Perfusion Metrics in Radiation Therapy Planning for Lung Cancer.

PURPOSE: Currently, several active clinical trials of functional lung avoidance radiation therapy using different imaging modalities for ventilation or perfusion are underway. Patients with lung cancer often show ventilation-perfusion mismatch, whereas the significance of dose-function metric remains unclear. The aim of the present study was to compare dose-ventilation metrics with dose-perfusion metrics for radiation therapy plan evaluation. METHODS AND MATERIALS: Pretreatment 4-dimensional computed tomography and 99mTc-macroaggregated albumin single-photon emission computed tomography perfusion images of 60 patients with lung cancer treated with radiation therapy were analyzed. Ventilation images were created using the deformable image registration of 4-dimensional computed tomography image sets and image analysis for regional volume changes as a surrogate for ventilation. Ventilation and perfusion images were converted into percentile distribution images. Analyses included Pearson's correlation coefficient and comparison of agreements between the following dose-ventilation and dose-perfusion metrics: functional mean lung dose and functional percent lung function receiving 5, 10, 20, 30, and 40 Gy (fV5, fV10, fV20, fV30, and fV40, respectively). RESULTS: Overall, the dose-ventilation metrics were greater than the dose-perfusion metrics (ie, fV20, 26.3% ± 9.9% vs 23.9% ± 9.8%). Correlations between the dose-ventilation and dose-perfusion metrics were strong (range, r = 0.94-0.97), whereas the agreements widely varied among patients, with differences as large as 6.6 Gy for functional mean lung dose and 11.1% for fV20. Paired t test indicated that the dose-ventilation and dose-perfusion metrics were significantly different. CONCLUSIONS: Strong correlations were present between the dose-ventilation and dose-perfusion metrics. However, the agreement between the dose-ventilation and dose-perfusion metrics widely varied among patients, suggesting that ventilation-based radiation therapy plan evaluation may not be comparable to that based on perfusion. Future studies should elucidate the correlation of dose-function metrics with clinical pulmonary toxicity metrics.

Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer.

Cone-beam computed tomography (CBCT) images acquired during radiotherapy may allow early response assessment. Previous studies have reported inconsistent findings on an association of CBCT-measured tumor volume changes with clinical outcomes. The purpose of this pilot study was twofold: (1) to characterize changes in CBCT-based radiomics features during treatment; and (2) to quantify the potential association of CBCT-based delta-radiomics features with overall survival in locally advanced lung cancer. We retrospectively identified 23 patients and calculated 658 radiomics features from each of 11 CBCT images per patient. Feature selection was performed based on repeatability, robustness against contouring uncertainties, and non-redundancy. We calculated the coefficient of determination (R 2) for the relationship between the actual feature value at the end of treatment and predicted value based on linear models fitted using features between the first and kth fractions. We also quantified the predictive ability for survival with two methods by: (1) comparing delta-radiomics features (defined as the mean change between the first and kth fractions) between two groups of patients divided by a cutoff survival time of 18 months using the t-test or Wilcoxon rank-sum test; and (2) quantifying univariate discrimination of two groups divided by the median of delta-radiomics feature. All selected seven radiomics features during treatment (as early as the 10th fraction) were predictive of those at the end of treatment (R 2 > 0.64). Three delta-radiomics features demonstrated significant differences (q < 0.05, as early as the 10th fraction) between the two groups of patients divided by the cutoff survival time. Two of those three features were also predictive of survival according to the log-rank statistics. We provided the first demonstration of a potential association of CBCT-based delta-radiomics features early during treatment with overall survival in locally advanced lung cancer. Our preliminary findings should be validated for a larger cohort of patients.

"Dose of the day" based on cone beam computed tomography and deformable image registration for lung cancer radiotherapy.

PURPOSE: Adaptive radiotherapy (ART) has potential to reduce toxicity and facilitate safe dose escalation. Dose calculations with the planning CT deformed to cone beam CT (CBCT) have shown promise for estimating the "dose of the day". The purpose of this study is to investigate the "dose of the day" calculation accuracy based on CBCT and deformable image registration (DIR) for lung cancer radiotherapy. METHODS: A total of 12 lung cancer patients were identified, for which daily CBCT imaging was performed for treatment positioning. A re-planning CT (rCT) was acquired after 20 Gy for all patients. A virtual CT (vCT) was created by deforming initial planning CT (pCT) to the simulated CBCT that was generated from deforming CBCT to rCT acquired on the same day. Treatment beams from the initial plan were copied to the vCT and rCT for dose calculation. Dosimetric agreement between vCT-based and rCT-based accumulated doses was evaluated using the Bland-Altman analysis. RESULTS: Mean differences in dose-volume metrics between vCT and rCT were smaller than 1.5%, and most discrepancies fell within the range of ± 5% for the target volume, lung, esophagus, and heart. For spinal cord Dmax , a large mean difference of -5.55% was observed, which was largely attributed to very limited CBCT image quality (e.g., truncation artifacts). CONCLUSION: This study demonstrated a reasonable agreement in dose-volume metrics between dose accumulation based on vCT and rCT, with the exception for cases with poor CBCT image quality. These findings suggest potential utility of vCT for providing a reasonable estimate of the "dose of the day", and thus facilitating the process of ART for lung cancer.

Development of a deformable lung phantom with 3D-printed flexible airways.

PURPOSE: Deformable lung phantoms have been proposed to investigate four-dimensional (4D) imaging and radiotherapy delivery techniques. However, most phantoms mimic only the lung and tumor without pulmonary airways. The purpose of this study was to develop a reproducible, deformable lung phantom with three-dimensional (3D)-printed airways. METHODS: The phantom consists of: (a) 3D-printed flexible airways, (b) flexible polyurethane foam infused with iodinated contrast agents, and (c) a motion platform. The airways were simulated using publicly available breath-hold computed tomography (CT) image datasets of a human lung through airway segmentation, computer-aided design modeling, and 3D printing with a rubber-like material. The lung was simulated by pouring liquid expanding foam into a mold with the 3D-printed airways attached. Iodinated contrast agents were infused into the lung phantom to emulate the density of the human lung. The lung/airways phantom was integrated into our previously developed motion platform, which allows for compression and decompression of the phantom in the superior-inferior direction. We quantified the reproducibility of the density (lung), motion/deformation (lung and airways), and position (airways) using breath-hold CT scans (with the phantom compressed and decompressed) repeated every two weeks over a 2-month period as well as 4D CT scans (with the phantom continuously compressed and decompressed) repeated twice over four weeks. The density reproducibility was quantified with a difference image (created by subtracting the rigidly registered baseline and the repeated images) in each of the compressed and decompressed states. Reproducibility of the motion/deformation was evaluated by comparing the baseline displacement vector fields (DVFs) derived from deformable image registration (DIR) between the compressed and decompressed phantom CT images with those of repeated scans and calculating the difference in the displacement vectors. Reproducibility of the airway position was quantified based on DIR between the baseline and repeated images. RESULTS: For the breath-hold CT scans, the mean difference in lung density between baseline and week 8 was -1.3 (standard deviation 33.5) Hounsfield unit (HU) in the compressed state and 0.4 (36.8) HU in the decompressed state, while large local differences were observed around the high-contrast structures (caused by small misalignments). By visual inspection, the DVFs (between the compressed and decompressed states) at baseline and last time point (week 8 for the breath-hold CT scans) demonstrated a similar pattern. The mean lengths of displacement vector differences between baseline and week 8 were 0.5 (0.4) mm for the lung and 0.3 (0.2) mm for the airways. The mean airway displacements between baseline and week 8 were 0.6 (0.5) mm in the compressed state and 0.6 (0.4) mm in the decompressed state. We also observed similar results for the 4D CT scans (week 0 vs week 4) as well as for the breath-hold CT scans at other time points (week 0 vs weeks 2, 4, and 6). CONCLUSIONS: We have developed a deformable lung phantom with 3D-printed airways based on a human lung CT image. Our findings indicate reproducible density, motion/deformation, and position. This phantom is based on widely available materials and technology, which represents advantages over other deformable phantoms.

Imaging of regional ventilation: Is CT ventilation imaging the answer? A systematic review of the validation data.

Computed Tomography Ventilation Imaging (CTVI) is an experimental imaging modality that derives regional lung function information from non-contrast respiratory-correlated CT datasets. Despite CTVI being extensively studied in cross-modality imaging comparisons, there is a lack of consensus on the state of its clinical validation in humans. This systematic review evaluates the CTVI clinical validation studies to date, highlights their common strengths and weaknesses and makes recommendations. We performed a PUBMED and EMBASE search of all English language papers on CTVI between 2000 and 2018. The results of these searches were filtered in accordance to a set of eligibility criteria and analysed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. One hundred and forty-four records were identified, and 66 full text records were reviewed. After detailed assessment, twenty-three full text papers met the selection criteria and were included in the final review. This included thirteen prospective studies, with 579 human subjects. Studies used diverse methodologies, with a large amount of heterogeneity between different studies in terms of the reference ventilation imaging modality (e.g. nuclear medicine, hyperpolarised gas MRI), imaging parameters, DIR algorithm(s) used, and ventilation metric(s) applied. The most common ventilation metrics used deformable image registration to evaluate the exhale-to-inhale motion field Jacobian determinant (DIR-Jac) or changes in air volume content based on Hounsfield Units (DIR-HU). The strength of correlation between CTVI and the reference ventilation imaging modalities was moderate to strong when evaluated at the lobar or global level, with the average ±â€¯S.D. (number of studies) linear regression correlation coefficients were 0.73 ±â€¯0.25 (n = 6) and 0.86 ±â€¯0.11 (n = 12) for DIR-Jac and DIR-HU respectively, and the SPC were 0.45 ±â€¯0.31 (n = 6) and 0.41 ±â€¯0.11 (n = 5) for DIR-Jac and DIR-HU respectively. We concluded that it is difficult to make a broad statement about the validity of CTVI due to the diverse methods used in the validation literature. Typically, CTVI appears to show reasonable cross-modality correlations at the lobar/whole lung level but poor correlations at the voxel level. Since CTVI is seeing new implementations in prospective trials, it is clear that refinement and standardization of the clinical validation methodologies are required. CTVI appears to be of relevance in radiotherapy planning, particularly in patients whose main pulmonary impairment is not a gas exchange problem but alternative imaging approaches may need to be considered in patients with other pulmonary diseases (i.e. restrictive or gas exchange problems).

A Feasibility Study of Single-inhalation, Single-energy Xenon-enhanced CT for High-resolution Imaging of Regional Lung Ventilation in Humans.

RATIONALE AND OBJECTIVES: The objective of this study was to assess the feasibility of single-inhalation xenon-enhanced computed tomography (XeCT) to provide clinically practical, high-resolution pulmonary ventilation imaging to clinics with access to only a single-energy computed tomography scanner, and to reduce the subject's overall exposure to xenon by utilizing a higher (70%) concentration for a much shorter time than has been employed in prior studies. MATERIALS AND METHODS: We conducted an institutional review board-approved prospective feasibility study of XeCT for 15 patients undergoing thoracic radiotherapy. For XeCT, we acquired two breath-hold single-energy computed tomography images of the entire lung with a single inhalation each of 100% oxygen and a mixture of 70% xenon and 30% oxygen, respectively. A video biofeedback system for coached patient breathing was used to achieve reproducible breath holds. We assessed the technical success of XeCT acquisition and side effects. We then used deformable image registration to align the breath-hold images with each other to accurately subtract them, producing a map of lung xenon distribution. Additionally, we acquired ventilation single-photon emission computed tomography-computed tomography (V-SPECT-CT) images for 11 of the 15 patients. For a comparative analysis, we partitioned each lung into 12 sectors, calculated the xenon concentration from the Hounsfield unit enhancement in each sector, and then correlated this with the corresponding V-SPECT-CT counts. RESULTS: XeCT scans were tolerated well overall, with a mild (grade 1) dizziness as the only side effect in 5 of the 15 patients. Technical failures in five patients occurred because of inaccurate breathing synchronization with xenon gas delivery, leaving seven patients analyzable for XeCT and single-photon emission computed tomography correlation. Sector-wise correlations were strong (Spearman coefficient >0.75, Pearson coefficient >0.65, P value <.002) for two patients for whom ventilation deficits were visibly pronounced in both scans. Correlations were nonsignificant for the remaining five who had more homogeneous XeCT ventilation maps, as well as strong V-SPECT-CT imaging artifacts attributable to airway deposition of the aerosolized imaging agent. Qualitatively, XeCT demonstrated higher resolution and no central airway deposition artifacts compared to V-SPECT-CT. CONCLUSIONS: In this pilot study, single-breath XeCT ventilation imaging was generally feasible for patients undergoing thoracic radiotherapy, using an imaging protocol that is clinically practical and potentially widely available. In the future, the xenon delivery failures can be addressed by straightforward technical improvements to the patient biofeedback coaching system.

The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging.

PURPOSE: CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities. METHODS: The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient rS , and Dice similarity coefficients evaluated for low function lung ( DSClow ) and high function lung ( DSChigh ). RESULTS: A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The rS and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for rS , 0.52 (0.36-0.67) for DSClow , and 0.45 (0.28-0.62) for DSChigh . All other algorithms exhibited at least one negative rS value, and/or one DSC value less than 0.5. CONCLUSIONS: The VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.

Changes in Regional Ventilation During Treatment and Dosimetric Advantages of CT Ventilation Image Guided Radiation Therapy for Locally Advanced Lung Cancer.

PURPOSE: Lung functional image guided radiation therapy (RT) that avoids irradiating highly functional regions has potential to reduce pulmonary toxicity following RT. Tumor regression during RT is common, leading to recovery of lung function. We hypothesized that computed tomography (CT) ventilation image-guided treatment planning reduces the functional lung dose compared to standard anatomic image-guided planning in 2 different scenarios with or without plan adaptation. METHODS AND MATERIALS: CT scans were acquired before RT and during RT at 2 time points (16-20 Gy and 30-34 Gy) for 14 patients with locally advanced lung cancer. Ventilation images were calculated by deformable image registration of four-dimensional CT image data sets and image analysis. We created 4 treatment plans at each time point for each patient: functional adapted, anatomic adapted, functional unadapted, and anatomic unadapted plans. Adaptation was performed at 2 time points. Deformable image registration was used for accumulating dose and calculating a composite of dose-weighted ventilation used to quantify the lung accumulated dose-function metrics. The functional plans were compared with the anatomic plans for each scenario separately to investigate the hypothesis at a significance level of 0.05. RESULTS: Tumor volume was significantly reduced by 20% after 16 to 20 Gy (P = .02) and by 32% after 30 to 34 Gy (P < .01) on average. In both scenarios, the lung accumulated dose-function metrics were significantly lower in the functional plans than in the anatomic plans without compromising target volume coverage and adherence to constraints to critical structures. For example, functional planning significantly reduced the functional mean lung dose by 5.0% (P < .01) compared to anatomic planning in the adapted scenario and by 3.6% (P = .03) in the unadapted scenario. CONCLUSIONS: This study demonstrated significant reductions in the accumulated dose to the functional lung with CT ventilation image-guided planning compared to anatomic image-guided planning for patients showing tumor regression and changes in regional ventilation during RT.

Technical Note: Correction for the effect of breathing variations in CT pulmonary ventilation imaging.

PURPOSE: The accuracy and precision of computed tomography (CT) pulmonary ventilation imaging with conventional CT scanners are limited by breathing variations. We propose a method to correct for the effect of breathing variations in CT ventilation imaging based on external respiratory signals acquired throughout a scan. METHODS: The proposed method is based on: (a) calculating voxel-by-voxel abdominal surface motion ranges using four-dimensional (4D) CT image datasets spatiotemporally correlated with external respiratory monitor data, and (b) applying the correction factor, which is defined as the ratio of the overall mean of the abdominal surface motion range in the lungs to that of each voxel, to the CT ventilation value. The performance of the proposed method was investigated by comparing voxel-wise correlations of the uncorrected and corrected CT ventilation images with single-photon emission CT (SPECT) ventilation images as a ground truth for nine patients. CT ventilation images were calculated by deformable image registration of the 4D-CT image datasets, followed by calculation of regional volume changes. A Steiger's Z-test was used to determine the statistical significance of the difference between the correlations for the uncorrected and corrected CT ventilation images. RESULTS: The proposed correction method resulted in significant increases (P < 0.05) in the correlation between CT and SPECT ventilation in three patients, trends toward significant increase (P: 0.13-0.18) in two patients, no significant differences in three patients, and a significantly decreased correlation in one patient. The average standard deviation of the abdominal surface motion range in three patients showing significant increases was 0.27 (range 0.10-0.37), which was greater than 0.17 (range 0.07-0.38) in the other six patients. CONCLUSIONS: The proposed method to correct for the effect of breathing variations could be readily implemented and has the potential to improve the accuracy of CT ventilation imaging as demonstrated in a nine-patient study.

Evaluating the Toxicity Reduction With Computed Tomographic Ventilation Functional Avoidance Radiation Therapy.

PURPOSE: Computed tomographic (CT) ventilation imaging is a new modality that uses 4-dimensional (4D) CT information to calculate lung ventilation. Although retrospective studies have reported on the reduction in dose to functional lung, no work to our knowledge has been published in which the dosimetric improvements have been translated to a reduction in the probability of pulmonary toxicity. Our work estimates the reduction in toxicity for CT ventilation-based functional avoidance planning. METHODS AND MATERIALS: Seventy previously treated lung cancer patients who underwent 4DCT imaging were used for the study. CT ventilation maps were calculated with 4DCT deformable image registration and a density change-based algorithm. Pneumonitis was graded on the basis of imaging and clinical presentation. Maximum likelihood methods were used to generate normal tissue complication probability (NTCP) models predicting grade 2 or higher (2+) and grade 3+ pneumonitis as a function of dose (V5 Gy, V10 Gy, V20 Gy, V30 Gy, and mean dose) to functional lung. For 30 patients a functional plan was generated with the goal of reducing dose to the functional lung while meeting Radiation Therapy Oncology Group 0617 constraints. The NTCP models were applied to the functional plans and the clinically used plans to calculate toxicity reduction. RESULTS: By the use of functional avoidance planning, absolute reductions in grade 2+ NTCP of 6.3%, 7.8%, and 4.8% were achieved based on the mean fV20 Gy, fV30 Gy, and mean dose to functional lung metrics, respectively. Absolute grade 3+ NTCP reductions of 3.6%, 4.8%, and 2.4% were achieved with fV20 Gy, fV30 Gy, and mean dose to functional lung. Maximum absolute reductions of 52.3% and 16.4% were seen for grade 2+ and grade 3+ pneumonitis for individual patients. CONCLUSION: Our study quantifies the possible toxicity reduction from CT ventilation-based functional avoidance planning. Reductions in grades 2+ and 3+ pneumonitis were 7.1% and 4.7% based on mean dose-function metrics, with reductions as high as 52.3% for individual patients. Our work provides seminal data for determining the potential toxicity benefit from incorporating CT ventilation into thoracic treatment planning.

Evaluating Which Dose-Function Metrics Are Most Critical for Functional-Guided Radiation Therapy.

PURPOSE: Four-dimensional (4D) computed tomography (CT) ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiation therapy in clinical trials. There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP). METHODS AND MATERIALS: Seventy lung cancer patients who underwent 4D CT imaging and pneumonitis grading were assessed. Pretreatment 4D CT scans of each patient were used to calculate ventilation images. We evaluated 3 types of dose-function metrics that combined the patient's 4D CT ventilation image and treatment planning dose distribution: (1) structure-based approaches; (2) image-based approaches using the dose-function histogram; and (3) nonlinear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability models predicting grade 3 or higher (ie, grade 3+) pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared with normal tissue complication probability models based on traditional, total lung dose metrics. RESULTS: The most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20 Gy (AUC, 0.70). Probabilities of grade 3+ RP of 20% and 10% correspond to V20 (percentage of volume receiving ≥20 Gy) to the functional subvolumes of 26.8% and 9.3%, respectively. Imaging-based analysis with the dose-function histogram and nonlinear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when we evaluated the percentage of functionality receiving ≥20 Gy. All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC of 0.55). CONCLUSIONS: A full range of dose-function metrics and functional thresholds was examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66-0.70), and all showed notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+ RP provides valuable data to inform the functional-guided radiation therapy process.

Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy.

BACKGROUND: Over 50% of patients who receive stereotactic body radiotherapy (SBRT) develop radiographic evidence of radiation-induced lung injury. Radiomics is an emerging approach that extracts quantitative features from image data, which may provide greater value and a better understanding of pulmonary toxicity than conventional approaches. We aimed to investigate the potential of computed tomography-based radiomics in characterizing post-SBRT lung injury. METHODS: A total of 48 diagnostic thoracic computed tomography scans (acquired prior to SBRT and at 3, 6, and 9 months post-SBRT) from 14 patients were analyzed. Nine radiomic features (ie, 7 gray level co-occurrence matrix [GLCM] texture features and 2 first-order features) were investigated. The ability of radiomic features to distinguish radiation oncologist-defined moderate/severe lung injury from none/mild lung injury was assessed using logistic regression and area under the receiver operating characteristic curve (AUC). Moreover, dose-response curves (DRCs) for radiomic feature changes were determined as a function of time to investigate whether there was a significant dose-response relationship. RESULTS: The GLCM features (logistic regression P-value range, 0.012-0.262; AUC range, 0.643-0.750) outperformed the first-order features (P-value range, 0.100-0.990; AUC range, 0.543-0.661) in distinguishing lung injury severity levels. Eight of 9 radiomic features demonstrated a significant dose-response relationship at 3, 6, and 9 months post-SBRT. Although not statistically significant, the GLCM features showed clear separations between the 3- or 6-month DRC and the 9-month DRC. CONCLUSION: Radiomic features significantly correlated with radiation oncologist-scored post-SBRT lung injury and showed a significant dose-response relationship, suggesting the potential for radiomics to provide a quantitative, objective measurement of post-SBRT lung injury.

Subpopulation-based correspondence modelling for improved respiratory motion estimation in the presence of inter-fraction motion variations.

Correspondence modelling between low-dimensional breathing signals and internal organ motion is a prerequisite for application of advanced techniques in radiotherapy of moving targets. Patient-specific correspondence models can, for example, be built prior to treatment based on a planning 4D CT and simultaneously acquired breathing signals. Reliability of pre-treatment-built models depends, however, on the degree of patient-specific inter-fraction motion variations. This study investigates whether motion estimation accuracy in the presence of inter-fraction motion variations can be improved using correspondence models that incorporate motion information from different patients. The underlying assumption is that inter-patient motion variations resemble patient-specific inter-fraction motion variations for subpopulations of patients with similar breathing characteristics. The hypothesis is tested by integrating a sparse manifold clustering approach into a regression-based correspondence modelling framework that allows for automated identification of patient subpopulations. The evaluation is based on a total of 73 lung 4D CT data sets, including two cohorts of patients with repeat 4D CT scans (cohort 1: 14 patients; cohort 2: ten patients). The results are consistent for both cohorts: The subpopulation-based modelling approach outperforms general population modelling (models built on all data sets available) as well as pre-treatment-built models trained on only the patient-specific motion information. The results thereby support the hypothesis and illustrate the potential of subpopulation-based correspondence modelling.

Single-energy computed tomography-based pulmonary perfusion imaging: Proof-of-principle in a canine model.

PURPOSE: Radiotherapy (RT) that selectively avoids irradiating highly functional lung regions may reduce pulmonary toxicity, which is substantial in lung cancer RT. Single-energy computed tomography (CT) pulmonary perfusion imaging has several advantages (e.g., higher resolution) over other modalities and has great potential for widespread clinical implementation, particularly in RT. The purpose of this study was to establish proof-of-principle for single-energy CT perfusion imaging. METHODS: Single-energy CT perfusion imaging is based on the following: (1) acquisition of end-inspiratory breath-hold CT scans before and after intravenous injection of iodinated contrast agents, (2) deformable image registration (DIR) for spatial mapping of those two CT image data sets, and (3) subtraction of the precontrast image data set from the postcontrast image data set, yielding a map of regional Hounsfield unit (HU) enhancement, a surrogate for regional perfusion. In a protocol approved by the institutional animal care and use committee, the authors acquired CT scans in the prone position for a total of 14 anesthetized canines (seven canines with normal lungs and seven canines with diseased lungs). The elastix algorithm was used for DIR. The accuracy of DIR was evaluated based on the target registration error (TRE) of 50 anatomic pulmonary landmarks per subject for 10 randomly selected subjects as well as on singularities (i.e., regions where the displacement vector field is not bijective). Prior to perfusion computation, HUs of the precontrast end-inspiratory image were corrected for variation in the lung inflation level between the precontrast and postcontrast end-inspiratory CT scans, using a model built from two additional precontrast CT scans at end-expiration and midinspiration. The authors also assessed spatial heterogeneity and gravitationally directed gradients of regional perfusion for normal lung subjects and diseased lung subjects using a two-sample two-tailed t-test. RESULTS: The mean TRE (and standard deviation) was 0.6 ± 0.7 mm (smaller than the voxel dimension) for DIR between pre contrast and postcontrast end-inspiratory CT image data sets. No singularities were observed in the displacement vector fields. The mean HU enhancement (and standard deviation) was 37.3 ± 10.5 HU for normal lung subjects and 30.7 ± 13.5 HU for diseased lung subjects. Spatial heterogeneity of regional perfusion was found to be higher for diseased lung subjects than for normal lung subjects, i.e., a mean coefficient of variation of 2.06 vs 1.59 (p = 0.07). The average gravitationally directed gradient was strong and significant (R(2) = 0.99, p < 0.01) for normal lung dogs, whereas it was moderate and nonsignificant (R(2) = 0.61, p = 0.12) for diseased lung dogs. CONCLUSIONS: This canine study demonstrated the accuracy of DIR with subvoxel TREs on average, higher spatial heterogeneity of regional perfusion for diseased lung subjects than for normal lung subjects, and a strong gravitationally directed gradient for normal lung subjects, providing proof-of-principle for single-energy CT pulmonary perfusion imaging. Further studies such as comparison with other perfusion imaging modalities will be necessary to validate the physiological significance.

The impact of audiovisual biofeedback on 4D functional and anatomic imaging: Results of a lung cancer pilot study.

BACKGROUND AND PURPOSE: The impact of audiovisual (AV) biofeedback on four dimensional (4D) positron emission tomography (PET) and 4D computed tomography (CT) image quality was investigated in a prospective clinical trial (NCT01172041). MATERIAL AND METHODS: 4D-PET and 4D-CT images of ten lung cancer patients were acquired with AV biofeedback (AV) and free breathing (FB). The 4D-PET images were analyzed for motion artifacts by comparing 4D to 3D PET for gross tumor volumes (GTVPET) and maximum standardized uptake values (SUVmax). The 4D-CT images were analyzed for artifacts by comparing normalized cross correlation-based scores (NCCS) and quantifying a visual assessment score (VAS). A Wilcoxon signed-ranks test was used for statistical testing. RESULTS: The impact of AV biofeedback varied widely. Overall, the 3D to 4D decrease of GTVPET was 1.2±1.3cm(3) with AV and 0.6±1.8cm(3) for FB. The 4D-PET increase of SUVmax was 1.3±0.9 with AV and 1.3±0.8 for FB. The 4D-CT NCCS were 0.65±0.27 with AV and 0.60±0.32 for FB (p=0.08). The 4D-CT VAS was 0.0±2.7. CONCLUSION: This study demonstrated a high patient dependence on the use of AV biofeedback to reduce motion artifacts in 4D imaging. None of the hypotheses tested were statistically significant. Future development of AV biofeedback will focus on optimizing the human-computer interface and including patient training sessions for improved comprehension and compliance.